Interactions Between Tourism Vessels and Humpback Whales in the Gulf of Tribugá, Colombia
Bibliographic record
Abstract
Whale-watching is a growing ecotourism activity in Colombia that offers economic benefits but may pose behavioral risks to humpback whales (Megaptera novaeangliae) if not properly managed. Although preventive recommendations exist to promote sustainable practices, their effectiveness remains largely unquantified in the Colombian North Pacific. This study evaluates adherence to whale-watching regulations and describes humpback whale behavioral responses to vessel presence in the Gulf of Tribugá, a key breeding and calving area. Data were collected from tourism vessels during July and September 2023, documenting 236 whales across 99 groups—71% of which included calves. The predominant whale response to vessels was neutral (74%), while evasive behaviors occurred in 22% of encounters, particularly among mother–calf pairs. Surface-active behavior was infrequent (22%) and most observed in calf–escort groups, serving as a supplementary indicator of behavioral state. Compliance with whale-watching recommendations varied: although most encounters involved a single vessel (57%) and averaged 16 min in duration, only 14% of vessels maintained the recommended minimum distance of 100 m. These findings highlight persistent gaps in regulatory adherence and underscore the need for strengthened enforcement and adaptive management to ensure sustainable whale-watching practices in Colombia’s North Pacific.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".